from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-11-28 14:07:17.337076
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Sat, 28, Nov, 2020
Time: 14:07:21
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -42.8804
Nobs: 124.000 HQIC: -44.0959
Log likelihood: 1291.96 FPE: 3.08781e-20
AIC: -44.9274 Det(Omega_mle): 1.53639e-20
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.628201 0.196737 3.193 0.001
L1.Burgenland 0.132382 0.089551 1.478 0.139
L1.Kärnten -0.311673 0.074767 -4.169 0.000
L1.Niederösterreich 0.025533 0.215499 0.118 0.906
L1.Oberösterreich 0.278196 0.177160 1.570 0.116
L1.Salzburg 0.139581 0.089871 1.553 0.120
L1.Steiermark 0.082127 0.127088 0.646 0.518
L1.Tirol 0.176472 0.084168 2.097 0.036
L1.Vorarlberg 0.018268 0.082355 0.222 0.824
L1.Wien -0.138965 0.169043 -0.822 0.411
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.661176 0.254166 2.601 0.009
L1.Burgenland -0.004522 0.115691 -0.039 0.969
L1.Kärnten 0.333918 0.096592 3.457 0.001
L1.Niederösterreich 0.092777 0.278405 0.333 0.739
L1.Oberösterreich -0.242752 0.228874 -1.061 0.289
L1.Salzburg 0.181197 0.116106 1.561 0.119
L1.Steiermark 0.230517 0.164186 1.404 0.160
L1.Tirol 0.139055 0.108737 1.279 0.201
L1.Vorarlberg 0.204787 0.106395 1.925 0.054
L1.Wien -0.568871 0.218388 -2.605 0.009
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.319229 0.084612 3.773 0.000
L1.Burgenland 0.105731 0.038514 2.745 0.006
L1.Kärnten -0.026522 0.032155 -0.825 0.409
L1.Niederösterreich 0.136625 0.092681 1.474 0.140
L1.Oberösterreich 0.270869 0.076192 3.555 0.000
L1.Salzburg -0.007496 0.038651 -0.194 0.846
L1.Steiermark -0.059524 0.054657 -1.089 0.276
L1.Tirol 0.097867 0.036198 2.704 0.007
L1.Vorarlberg 0.150597 0.035419 4.252 0.000
L1.Wien 0.020596 0.072701 0.283 0.777
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.187115 0.100299 1.866 0.062
L1.Burgenland 0.004769 0.045654 0.104 0.917
L1.Kärnten 0.032401 0.038117 0.850 0.395
L1.Niederösterreich 0.085129 0.109864 0.775 0.438
L1.Oberösterreich 0.355640 0.090318 3.938 0.000
L1.Salzburg 0.089310 0.045818 1.949 0.051
L1.Steiermark 0.197415 0.064791 3.047 0.002
L1.Tirol 0.027898 0.042910 0.650 0.516
L1.Vorarlberg 0.115732 0.041985 2.756 0.006
L1.Wien -0.096685 0.086180 -1.122 0.262
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.795289 0.214047 3.715 0.000
L1.Burgenland 0.048913 0.097430 0.502 0.616
L1.Kärnten -0.017627 0.081345 -0.217 0.828
L1.Niederösterreich -0.100999 0.234460 -0.431 0.667
L1.Oberösterreich 0.065447 0.192747 0.340 0.734
L1.Salzburg 0.037850 0.097779 0.387 0.699
L1.Steiermark 0.106560 0.138270 0.771 0.441
L1.Tirol 0.227622 0.091573 2.486 0.013
L1.Vorarlberg 0.034916 0.089601 0.390 0.697
L1.Wien -0.168730 0.183916 -0.917 0.359
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.187380 0.148194 1.264 0.206
L1.Burgenland -0.042804 0.067455 -0.635 0.526
L1.Kärnten -0.014316 0.056319 -0.254 0.799
L1.Niederösterreich 0.196115 0.162326 1.208 0.227
L1.Oberösterreich 0.396077 0.133447 2.968 0.003
L1.Salzburg -0.036857 0.067696 -0.544 0.586
L1.Steiermark -0.055506 0.095730 -0.580 0.562
L1.Tirol 0.198675 0.063400 3.134 0.002
L1.Vorarlberg 0.055749 0.062034 0.899 0.369
L1.Wien 0.126086 0.127333 0.990 0.322
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.305488 0.188433 1.621 0.105
L1.Burgenland 0.065449 0.085771 0.763 0.445
L1.Kärnten -0.086503 0.071611 -1.208 0.227
L1.Niederösterreich -0.140994 0.206404 -0.683 0.495
L1.Oberösterreich -0.104809 0.169683 -0.618 0.537
L1.Salzburg 0.000008 0.086078 0.000 1.000
L1.Steiermark 0.374663 0.121724 3.078 0.002
L1.Tirol 0.540444 0.080615 6.704 0.000
L1.Vorarlberg 0.230726 0.078879 2.925 0.003
L1.Wien -0.174530 0.161908 -1.078 0.281
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.112587 0.217046 0.519 0.604
L1.Burgenland 0.022037 0.098795 0.223 0.823
L1.Kärnten -0.064105 0.082485 -0.777 0.437
L1.Niederösterreich 0.255710 0.237745 1.076 0.282
L1.Oberösterreich 0.020361 0.195448 0.104 0.917
L1.Salzburg 0.231258 0.099149 2.332 0.020
L1.Steiermark 0.155517 0.140207 1.109 0.267
L1.Tirol 0.053599 0.092856 0.577 0.564
L1.Vorarlberg 0.014767 0.090856 0.163 0.871
L1.Wien 0.209349 0.186493 1.123 0.262
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.618095 0.121139 5.102 0.000
L1.Burgenland -0.009596 0.055140 -0.174 0.862
L1.Kärnten -0.005011 0.046037 -0.109 0.913
L1.Niederösterreich -0.060909 0.132692 -0.459 0.646
L1.Oberösterreich 0.276547 0.109084 2.535 0.011
L1.Salzburg 0.002749 0.055337 0.050 0.960
L1.Steiermark 0.010128 0.078253 0.129 0.897
L1.Tirol 0.077437 0.051825 1.494 0.135
L1.Vorarlberg 0.194435 0.050709 3.834 0.000
L1.Wien -0.094960 0.104086 -0.912 0.362
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.092404 -0.044105 0.201359 0.235012 0.016743 0.063310 -0.131588 0.112723
Kärnten 0.092404 1.000000 -0.063913 0.179280 0.074235 -0.166190 0.188815 0.011929 0.268129
Niederösterreich -0.044105 -0.063913 1.000000 0.233394 0.072598 0.156289 0.071123 0.051534 0.358870
Oberösterreich 0.201359 0.179280 0.233394 1.000000 0.248013 0.263867 0.071140 0.059354 0.044917
Salzburg 0.235012 0.074235 0.072598 0.248013 1.000000 0.142712 0.037332 0.079707 -0.062005
Steiermark 0.016743 -0.166190 0.156289 0.263867 0.142712 1.000000 0.095560 0.092498 -0.195901
Tirol 0.063310 0.188815 0.071123 0.071140 0.037332 0.095560 1.000000 0.132943 0.091667
Vorarlberg -0.131588 0.011929 0.051534 0.059354 0.079707 0.092498 0.132943 1.000000 0.080842
Wien 0.112723 0.268129 0.358870 0.044917 -0.062005 -0.195901 0.091667 0.080842 1.000000